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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17964 |
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| _version_ | 1866912168719417344 |
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| author | Seabra, Antony Cavalcante, Claudio Nepomuceno, Joao Lago, Lucas Ruberg, Nicolaas Lifschitz, Sergio |
| author_facet | Seabra, Antony Cavalcante, Claudio Nepomuceno, Joao Lago, Lucas Ruberg, Nicolaas Lifschitz, Sergio |
| contents | We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source question-answer systems. This methodology is designed to integrate information from diverse data sources, including unstructured documents (PDFs) and structured databases, through a coordinated multi-agent orchestration and dynamic retrieval approach. Our methodology leverages specialized agents-such as SQL agents, Retrieval-Augmented Generation (RAG) agents, and router agents - that dynamically select the most appropriate retrieval strategy based on the nature of each query. To further improve accuracy and contextual relevance, we employ dynamic prompt engineering, which adapts in real time to query-specific contexts. The methodology's effectiveness is demonstrated within the domain of Contract Management, where complex queries often require seamless interaction between unstructured and structured data. Our results indicate that this approach enhances response accuracy and relevance, offering a versatile and scalable framework for developing question-answer systems that can operate across various domains and data sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models Seabra, Antony Cavalcante, Claudio Nepomuceno, Joao Lago, Lucas Ruberg, Nicolaas Lifschitz, Sergio Artificial Intelligence We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source question-answer systems. This methodology is designed to integrate information from diverse data sources, including unstructured documents (PDFs) and structured databases, through a coordinated multi-agent orchestration and dynamic retrieval approach. Our methodology leverages specialized agents-such as SQL agents, Retrieval-Augmented Generation (RAG) agents, and router agents - that dynamically select the most appropriate retrieval strategy based on the nature of each query. To further improve accuracy and contextual relevance, we employ dynamic prompt engineering, which adapts in real time to query-specific contexts. The methodology's effectiveness is demonstrated within the domain of Contract Management, where complex queries often require seamless interaction between unstructured and structured data. Our results indicate that this approach enhances response accuracy and relevance, offering a versatile and scalable framework for developing question-answer systems that can operate across various domains and data sources. |
| title | Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.17964 |